Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
The data mainly comes from inbound and outbound registration, rotation operation records, grain storage safety inspection forms, and policy-based grain purchase and storage approval documents generated during daily grain depot operations. Updates occur in real time after each single operation is completed. Full warehouse inventory summary data is updated after monthly inventory checks. Most documents are structured forms and official documents. Fixed fields include grain category, storage quantity, warehouse number, and inspection project values. Units are mostly tons, kilograms, and cubic meters. There are no percentage-based statistical fields.
What constraints these characteristics impose on model access and configuration
Structured forms and fixed fields require that documents generated by the model strictly match preset field formats. Avoid generating content without corresponding fields. Configure output structure limits. Real-time updated inventory data requires the model to obtain the latest version of content when calling the knowledge base. Configure real-time synchronization rules for context. Mixed input of multiple document types (forms and official documents) requires the model to support parsing of different formats. Adjust the model's document splitting and recall logic. The requirement for unified units requires the model to strictly use specified units such as tons and kilograms when generating materials. Add unit verification rules in the configuration.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Grain reserve documents often include multiple historical ledgers and inspection reports. Sufficient context is needed to link cross-document inventory data |
maxTokens | 2000–3000 characters | The output length for single document drafting must match the standard length of internal grain depot documents, to avoid being too long or too short |
quoteMaxToken | 1500 token | Sufficient historical inventory data must be recalled for reference, without occupying too much of the model's output quota |
temperature | 0.3–0.5 | Document drafting requires strict rigor, to avoid generating overly divergent content |
AIPROXY_API_ENDPOINT | Fill in the dedicated API access address for the grain reserve system | Call compliant model services through a dedicated channel to ensure data security |
AIPROXY_API_TOKEN | Use the dedicated key assigned by the grain reserve system | Ensure the security of access permissions and prevent unauthorized access |
Three common configuration errors
- An
Api response erroroccurs during model testing, with a prompt indicating abnormal interface path. The cause is incorrect configuration of theAIPROXY_API_ENDPOINTparameter, using an unofficial interface address. - Non-specified units are mixed in generated documents, such as writing tons as liters. The cause is failure to configure unit verification rules in the settings, so the model does not match the unit requirements of the grain reserve system.
- After enabling the model thought output switch, generated documents include
<think>tag content. The cause is incorrect configuration of the model's output format parameters, failing to turn off the model's internal thought process output.
How to confirm the configuration is complete
- Call the FastGPT built-in model testing interface, check that the returned response status code is 200, to confirm normal interface connectivity.
- Input structured content from a grain depot inspection form, check whether the generated document includes preset fixed fields, to confirm that the format matching rule is effective.
- Call the knowledge base recall function, check whether the returned content is the latest inventory data, to confirm that the context synchronization configuration is effective.
- Generate a simulated rotation plan document, check whether the specified units are used in the output content, to confirm that the unit verification rule is effective.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.